Finance 2: Dynamic Portfolio Choice (Fin2)

Course content

See the "Knowledge" part of the learning outcome below.

Education

MSc Programme in Mathematics-Economics

Learning outcome

Competencies

  1. Formulate and analyze decision problems (investment/consumption and optimal stopping) in a stochastic multi-period setting.
  2. Analyze model consequences “with numbers”; algorithmically, experimentally or empirically. (As well as understand why these three things are different concepts.)
  3. Acquire the confidence to read presentations of the same – or almost the same – problem in the literature. Know that notation, motivation, and rigour varies and that there is rarely a gospel.   

Skills

  • Rigorously prove optimality principles and conditions for stochastic control problems in (discrete time, finite space)-multi-period setting.
  • Explicitly solve simple investment/consumption and optimal stopping problems.   
  • Derive (with pen and paper), analyze (with a computer) and explain (in plain English) model implications; be they quantitative or qualitative, be they regarding policy, equilibrium, or empirics.

Knowledge

  • Maximization of expected utility and (partial) equilibrium in one-period models, including betting against beta.
  • Multi-period optimal portfolio choice. The martingale method vs. dynamic programming/the Bellman equation.
  • Explicit solutions with HARA utility and binomial(‘ish) stock dynamics. 
  • Properties and consequences of solutions; myopia and constant weights, C-CAPM, the equity premium puzzle.
  • Optimal stopping and the hedging and pricing of American options.

4 hours of lectures and 2 hours of tutorials per week for 7 weeks.

A bachelor degree in Mathematics-Economics.

Academic qualifications equivalent to a BSc degree is recommended.

Oral
Collective
Feedback by final exam (In addition to the grade)
ECTS
7,5 ECTS
Type of assessment
Oral examination, 20 minutes
Without preparation time, but "open book" (i.e. "all aids allowed").
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
External censorship
Criteria for exam assessment

The student must in a satisfactory way demonstrate that he/she has mastered the learning outcome of the course.

Single subject courses (day)

  • Category
  • Hours
  • Lectures
  • 28
  • Theory exercises
  • 14
  • Exam
  • 1
  • Preparation
  • 163
  • English
  • 206

Kursusinformation

Language
English
Course number
NMAA09045U
ECTS
7,5 ECTS
Programme level
Full Degree Master
Duration

1 block

Schedulegroup
C
Capacity
No limit
Studyboard
Study Board of Mathematics and Computer Science
Contracting department
  • Department of Mathematical Sciences
Contracting faculty
  • Faculty of Science
Course Coordinator
  • Rolf Poulsen   (4-7774716b457266796d33707a336970)
Phone: +45 35 32 06 85 office, 04.4.11
Saved on the 12-06-2019

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